https://www.jmlr.org/papers/v23/21-1060.html>. This package is its R interface. The package implements and generalizes algorithms designed in <doi:10.1073/pnas.2014241117> that exploits a novel sequencing-and-splicing technique to guarantee exact support recovery and globally optimal solution in polynomial times for linear model. It also supports best subset selection for logistic regression, Poisson regression, Cox proportional hazard model, Gamma regression, multiple-response regression, multinomial logistic regression, ordinal regression, (sequential) principal component analysis, and robust principal component analysis. The other valuable features such as the best subset of group selection <doi:10.1287/ijoc.2022.1241> and sure independence screening <doi:10.1111/j.1467-9868.2008.00674.x> are also provided.">

abess: Fast Best Subset Selection (original) (raw)

Extremely efficient toolkit for solving the best subset selection problem <https://www.jmlr.org/papers/v23/21-1060.html>. This package is its R interface. The package implements and generalizes algorithms designed in <doi:10.1073/pnas.2014241117> that exploits a novel sequencing-and-splicing technique to guarantee exact support recovery and globally optimal solution in polynomial times for linear model. It also supports best subset selection for logistic regression, Poisson regression, Cox proportional hazard model, Gamma regression, multiple-response regression, multinomial logistic regression, ordinal regression, (sequential) principal component analysis, and robust principal component analysis. The other valuable features such as the best subset of group selection <doi:10.1287/ijoc.2022.1241> and sure independence screening <doi:10.1111/j.1467-9868.2008.00674.x> are also provided.

Version: 0.4.10
Depends: R (≥ 3.1.0)
Imports: Rcpp, MASS, methods, Matrix
LinkingTo: Rcpp, RcppEigen
Suggests: testthat, knitr, rmarkdown
Published: 2025-04-05
DOI: 10.32614/CRAN.package.abess
Author: Jin Zhu ORCID iD [aut, cre], Zezhi Wang [aut], Liyuan Hu [aut], Junhao Huang [aut], Kangkang Jiang [aut], Yanhang Zhang [aut], Borui Tang [aut], Shiyun Lin [aut], Junxian Zhu [aut], Canhong Wen [aut], Heping Zhang ORCID iD [aut], Xueqin Wang ORCID iD [aut], spectra contributors [cph] (Spectra implementation)
Maintainer: Jin Zhu
BugReports: https://github.com/abess-team/abess/issues
License: GPL (≥ 3) | file
Copyright: see file
URL: https://github.com/abess-team/abess,https://abess-team.github.io/abess/,https://abess.readthedocs.io
NeedsCompilation: yes
Citation: abess citation info
Materials: README, NEWS
In views: MachineLearning
CRAN checks: abess results

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